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12th International Conference on CYBER Technology in automation, control, and Intelligent systems, CYBER 2022

作者： Sun, Shijie Chen, Li Liang, Wei An, Haibo Zhang, Yinlong School of Artificial Intelligence Shenyang University of Technology Shenyang110020 China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China Science and Technology on Information Systems Engineering Laboratory The 28th Research Institute of Cetc Jiangsu Nanjing210007 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

VSLAM shows promising potential for AGV navigation in digital workshops, thanks to its merits in low cost, easy configuration, high flexibility, and high accuracy. However, the AGV visual perception system could be easily affected by lighting changes, and body abrupt motions. The state-of-the-art visual feature matching methods may fail to fulfill the navigation tasks in presence of these challenging issues. In order to solve these problems, we propose a feature matching method based on feature space constraints and inertial assistance. In order to reduce the computational time, the spatial constraints are designed to delete the obvious false matches. Besides, the Inertia Assistance Visual Sample Consensus (IAVSAC) is proposed to reduce the computational time in discriminating the wrong feature matches. In order to ensure the reliability of the inertial data, the analysis of IMU noise has been performed. The best IMU configuration with the smallest noises is designed. Our proposed method has been evaluated on the developed AGV navigation platform. The results show that the feature matching accuracy rate and computational time of ours are more favorable than those of RANSAC and MSAC. © 2022 IEEE.

关键词： Navigation

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An adaptive federated control strategy for participant selection in multi-client collaboration 1

An adaptive federated control strategy for participant selec...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Chen, Yizhu Du, Xiaoming Wang, Xiao Zhang, Jun Jason Wang, Fei-Yue Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Economics and Management University of Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Qingdao China School of Electrical Engineering and Automation Wuhan University Wuhan China

ISBN: (纸本)9781665433372

The federated ecology provides a new paradigm for breaking the isolated data island problem and fully activating the potential of big data and artificial intelligence, especially in multi-client collaboration tasks. Participant selection strategy in multi-client collaboration is the main limitation for increasing the convergence speed and lowing the communication costs. However, in the face of the unbalanced and non-IID data distributions, the performance of federated optimization algorithms will also decrease. To solve the above problems, we propose an adaptive federated control strategy for participant selection in multi-client collaboration based on the Mann-Kendall test, named FedMK. By adaptively selecting weak participants for training rather than random selection, FedMK can speed up model convergence and reduce communication costs. Experiment results show that our method outperforms the baseline method in non-IID scenarios, and the number of communication rounds on CIFAR-10 and synthetic datasets reduced by more than 15% and 10 %, 9 respectively. © 2021 IEEE.

关键词： Stream flow

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A Novel Two-Stage Framework for 2D/3D Registration in Neurological Interventions

A Novel Two-Stage Framework for 2D/3D Registration in Neurol...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Huang, De-Xing Zhou, Xiao-Hu Xie, Xiao-Liang Liu, Shi-Qi Feng, Zhen-Qiu Hao, Jian-Long Hou, Zeng-Guang Ma, Ning Yan, Long The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The School of Information Shanxi University of Finance and Economics Taiyuan030006 China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China The Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology Taipa China The Department of Interventional Neuroradiology Beijing Tiantan Hospital Capital Medical University Beijing100070 China

ISBN: (纸本)9781665481090

2D/3D medical image registration of pre-operative volumes and intra-operative images plays an important role in neurological interventions. However, vast space of transformation parameters makes this task incredibly challenging. In this paper, a novel two-stage framework for 2D/3D registration is proposed. It consists of two basic modules: CNN regression for preliminarily estimating transformation parameters, and centroid alignment for further reducing estimation errors to get refined images. Experimental results on two patients show the proposed framework can achieve superior performances to baseline methods with an inference rate of 22 FPS, satisfying real-time requirements (612 FPS) in clinical practice. Extensive ablation studies prove the effectiveness of centroid alignment in significantly improving registration accuracies. These promising results indicate the framework has the potential to be integrated into navigation frameworks for neurological interventions, facilitating treatments of physicians. © 2022 IEEE.

关键词： Medical imaging

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Anomaly Detection Based on Principle of Justifiable Granularity and Probability Density Estimation

Anomaly Detection Based on Principle of Justifiable Granular...

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2023 China automation Congress, CAC 2023

作者： Du, Sheng Ma, Xian Li, Xiang Wu, Min Cao, Weihua Pedrycz, Witold School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul Sariyer Turkey

ISBN: (纸本)9798350303759

Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. First, time series data are transformed into a two-dimensional information granule by the principle of justifiable granularity. Then, the test statistic is constructed, and the probability density and cumulative distribution functions of the test statistic are calculated. Next, the confidence level determines the test threshold. Finally, the time series data of a key parameter in the sintering process is used as a case study. The experimental result demonstrates that the proposed approach can detect abnormal time series data effectively, providing an accurate and effective solution for detecting time series anomalies in industrial processes. © 2023 IEEE.

关键词： Time series

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Quantifying the impacts of COVID-19 on Sustainable Development Goals using machine learning models

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Fundamental Research 2024年第4期4卷 890-897页

作者： Chenyang Shuai Bu Zhao Xi Chen Jianguo Liu Chunmiao Zheng Shen Qu Jian-Ping Zou Ming Xu School of Management Science and Real Estate Chongqing UniversityChongqing 400044China bSchool for Environment and Sustainability University of MichiganAnn ArborMI 489United Stats Michigan Institute for Computational Discovery&Engineering University of MichiganAnn ArborMI 48109United States College of Economics and Management Southwest UniversityChongqing 400044China Center for Systems Integration and Sustainability Department of Fisheries and WildlifeMichigan State UniversityEast LansingMI 48824United States Guangdong Provincial Key Laboratory of Soil and Groundwater Pollution Control School of Environmental Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China School of Management and Economics Beijing Institute of TechnologyBeijing 100081China Center for Energy&Environmental Policy Research Beijing Institute of TechnologyBejing 100081China Key Laboratory of Jiangxi Province for Persistent Plutants Control and Resources Recycle Nanchang Hangkong UniversityNanchang 330063China Department of Civil and Environmental Engineering University of MichiganAnn ArborMI 48109United States

The COvID-19 pandemic has posed severe threats to global sustainable ***,a comprehensive quantitative assessment of the impacts of COVID-19 on Sustainable Development Goals(SDGs)is still *** research quantified the post-COVID-19 SDG progress from 2020 to 2024 using projected GDP growth and population and machine learning models including support vector machine,random forest,and extreme gradient *** results show that the overall SDG performance declined by 7.7%in 2020 at the global scale,with 12 socioeconomic SDG performance decreasing by 3.0%-22.3%and 4 environmental SDG performance increasing by 1.6%-9.2%.By 2024,the progress of 12 SDGs will lag behind for one to eight years compared to their pre-COVID-19 trajectories,while extra time will be gained for 4 environment-related ***,the pandemic will cause more impacts on countries in emerging markets and developing economies than those on advanced economies,and the latter will recover more quickly to be closer to their pre-covID-19 trajectories by ***-COVID-19 economic recovery should emphasize in areas that can help decouple economic growth from negative environmental *** results can help government and non-state stakeholders identify critical areas for targeted policy to resume and speed up the progress to achieve SDGs by 2030.

关键词： Sustainable development goals COVID-19 Machine learning Global development Sustainable development assessment

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Finite-Time Synchronization of Flux-controlled complex-Valued Memristive Neural Networks

Finite-Time Synchronization of Flux-Controlled Complex-Value...

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Neuromorphic Computing (ICNC), International Conference on

作者： Yonghuan Wang Leimin Wang Genping Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China

In this paper, by using the flux-controlled memristor model, the finite-time synchronization problem of delayed complex-valued memristive neural networks (MCNNs) is studied. Firstly, according to the proposed memristor model, we model the MCNNs as continuous systems on voltage-flux-time $(\wp, \varpi, t)$ domain. Then, we design a class of approaches to realize state synchronization between the drive and response systems, and the corresponding synchronization conditions are achieved. Finally, the effectiveness of results is illustrated with the simulations.

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Locomotion control of a hybrid propulsion biomimetic underwater vehicle via deep reinforcement learning

Locomotion control of a hybrid propulsion biomimetic underwa...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Zhang, Tiandong Wang, Rui Wang, Yu Wang, Shuo Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9781665436786

This paper presents a novel deep reinforcement learning (DRL) method to solve the locomotion control problem of the biomimetic underwater vehicle (BUV) with hybrid propulsion, in order to meet the challenge of intractable multi-fins coordination and the complex hydrodynamic model. The system overview of the BUV, named RoboDact, with two flexible long fins and a double-joint fishtail as hybrid propulsion, is introduced. After that, the locomotion control problem is modeled as a Markov decision process (MDP) to be solved. Therefore, the locomotion control method based on soft actor-critic (SAC, a novel DRL algorithm) is proposed. The simulation environment is established based on the kinetic model for interaction. Finally, the feasibility and effectiveness of the proposed control method is demonstrated after extensive simulations. It will provide rich insights into the coordination control of biomimetic underwater vehicles. © 2021 IEEE.

关键词： Reinforcement learning

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Multiplicative Fault Detection and Isolation in Dynamic systems Using Data-Driven K-Gap Metric based kNN Algorithm

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IFAC-PapersOnLine 2022年第6期55卷 169-174页

作者： Caroline Charlotte Zhu Linlin Li Steven X. Ding Institute of Automatic Control and Complex Systems (AKS) University of Duisburg-Essen Duisburg 47057 Germany Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing 100083 Beijing China

In this paper, a fault detection and isolation scheme for multiplicative faults in dynamic systems based on data-driven K-Gap metric and k-nearest neighbour (kNN) classification is proposed. To detect multiplicative faults, the standard classification task of kNN is studied from the viewpoint of system analysis. To this end, the data-driven stable kernel representation based on input/output data is presented for feature extraction capturing the dynamic of linear time-invariant (LTI) systems. Data-driven K-Gap metric is used as an alternative tool for distance measure between two kernel subspaces in the kNN algorithm. A simulation example on the three-tank system (DTS200) demonstrates the successful detection and isolation of various multiplicative faults

关键词： Fault detection isolation multiplicative faults data-driven methods multiclass classification process monitoring K-Gap gap metrics

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Improved Detection of Transmission Tower Equipment Using YOLOv7 with Saliency Data Augmentation

Improved Detection of Transmission Tower Equipment Using YOL...

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Clean Energy and Electric Power Engineering (ICCEPE), International Conference on

作者： Yingying Xu Chunhe Song Yong Sun Shimao Yu State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350390315

ISBN: (纸本)9798350390322

Transmission towers are critical infrastructure for power transmission, and the reliable operation of their equipment is essential to ensure electricity supply. However, the detection of transmission tower equipment faces challenges such as target overlap, large variations in size, small targets, and class imbalance due to the characteristics of the equipment. To tackle the identified issues, this study introduces a method based on YOLOv7 for the detection of transmission tower components, utilizing saliency-based data augmentation. Initially, an approach focusing on importance is employed to augment the sample size in categories with limited numbers, thereby enhancing the effectiveness of detection. Secondly, an adaptive adjustment module is introduced into the multi-head self-attention mechanism to dynamically adjust attention allocation based on the actual size of the targets and integrate contextual information, thus better handling targets with large size variations and overlaps. Finally, incorporating dynamic convolution within the backbone network significantly improves the ability to extract features from small objects. The outcomes of the experiments confirm the efficiency of the suggested approach in the smart surveillance of devices within intricate transmission settings, offering a reliable solution for maintaining power supply dependability.

关键词： Surveillance Poles and towers Data enhancement Power transmission Object detection Manuals Data augmentation Feature extraction Reliability Saliency detection

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Probabilistic Graph Matching with Multiplicative Updating Algorithm for Correspondence between Remote Sensing Images

Probabilistic Graph Matching with Multiplicative Updating Al...

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Information Communication and Software Engineering (ICICSE), IEEE International Conference on

作者： Jing Yang Xu Yang Zhang-Bing Zhou Zhi-Yong Liu School of Information Engineering China University of Geosciences(Beijing) State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Information Engineering China University of Geosciences(Beijing) TELECOM SudParis Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology Beijing China

ISBN: (纸本)9781665430302

Point correspondence is an important problem in remote sensing images matching task which lays the foundation for the image registration task. Due to the existence of the rotation transformation and the large scale, and the change of scenery between the two obtained images, the point correspondence problem is still challenging. Tradition algorithms such as the SIFT and SURF aim at using the point appearance to find the point correspondence. However, when the above situations happens, the appearance based algorithms will fail to solve the problem. In this paper, the structural information between points is introduced to the correspondence problem and a spectral graph matching algorithm is proposed based on the probabilistic scheme and the multiplicative updating scheme. The proposed algorithm has three advantages compared with the traditional algorithms. First, incorporating the structural cues to the matching problem makes it more robust to the large scale and the geometric transformation. Second, the best assignment between points which is interpreted by a probabilistic manner is figured out by ranking the probabilities. Third, the optimization problem is solved by an approximate spectral method efficiently and a multiplicative updating algorithm is introduced to ensure the final discrete solution. Experiments on the real remote sensing images validate the effectiveness of the proposed algorithm.

关键词： Image registration Software algorithms Approximation algorithms Probabilistic logic Sensors Internet Task analysis

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